In this example, we replaced theNaNvalues with0usingfillna(). Replace Missing Values With Mean, Median and Mode A more refined approach is to replace missing values with the mean, median, or mode of the remainin
data = pd.read_csv("employees.csv") # creating bool series True for NaN values bool_series = pd.notnull(data["Gender"]) # filtering data # displayind data only with Gender = Not NaN data[bool_series] 产出: 如输出映像所示,只有具有Gender = NOT NULL都会显示。 使用fillna(), replace()...
Thefillna(0)method replaces all missing values with 0. This is useful for initializing missing data. Forward Fill Missing Values This example demonstrates forward filling missing values. fillna_ffill.py import pandas as pd import numpy as np data = { 'A': [1, np.nan, np.nan, 4], 'B'...
# importing pandas packageimportpandasaspd# making data frame from csv filedata=pd.read_csv("employees.csv")# creating bool series True for NaN valuesbool_series=pd.notnull(data["Gender"])# filtering data# displaying data only with Gender = Not NaNdata[bool_series] 使用fillna()、replace()...
# Fill missing values in the dataset with a specific valuedf = df.fillna(0)# Replace missing values in the dataset with mediandf = df.fillna(df.median())# Replace missing values in Order Quantity column with the mean of Order Quantitiesdf['Order Quantity'].fillna(df["Order Quantity"]....
代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Fill missing values in the dataset with a specific value df = df.fillna(0) # Replace missing values in the dataset with median df = df.fillna(df.median()) # Replace missing values in Order Quantity column with the mean of Order Quant...
# Replace missing values with a number df['ST_NUM'].fillna(125, inplace=True)# 125替换缺失值 或者可以用赋值的方式: # Location based replacement df.loc[2,'ST_NUM']=125 用该列的中值替换缺失值: # Replace using median median=df['NUM_BEDR...
# Replace missing values with a number df['ST_NUM'].fillna(125, inplace=True) # 125替换缺失值 1. 2. 或者可以用赋值的方式: # Location based replacement df.loc[2,'ST_NUM'] = 125 1. 2. 用该列的中值替换缺失值: # Replace using median ...
# Replace missing values with a number df['ST_NUM'].fillna(125, inplace=True)# 125替换缺失值 或者可以用赋值的方式: # Location based replacement df.loc[2,'ST_NUM']=125 用该列的中值替换缺失值: # Replace using median median=df['NUM_BEDROOMS'].median() ...
# Replace missing values with a number df['ST_NUM'].fillna(125, inplace=True) 或者我们可以通过准确定位来替换缺失值: # Location based replacement df.loc[2,'ST_NUM'] = 125 替换缺失值的一种非常常见的方法是使用中位数: # Replace using median ...